Method and apparatus for inspecting an object employing machine vision

ABSTRACT

A machine vision system including a digital camera can be employed to inspect an object in a field of view, including capturing an original digital image including a multiplicity of pixels and associated light intensities for the field of view including the object. A bitmap image file is generated, and a mean value and a standard deviation of the light intensities of the multiplicity of pixels of the bitmap image file for the original digital image are dynamically determined. New image files are generated, each including a portion of the multiplicity of pixels having associated light intensities within a prescribed range of light intensities defined by the mean value and the standard deviation. Line segments are extracted from each of the new image files, and the extracted line segments from the new image files are merged and clustered to generate integral lines based thereon.

TECHNICAL FIELD

The present disclosure relates to machine vision systems.

BACKGROUND

Imaging systems are employed in manufacturing environments toautomatically inspect stationary components. Imaging systems seek todetermine three-dimensional (3D) information about an object in a fieldof view for quality inspection, reverse engineering, robotics andsimilar systems. Such systems employ structural lighting as part of astereo imaging system to project light onto a field of view, capturingdigital images of an object in the field of view and employing geometricmethodology and decoding techniques to calculate image depth(s) usingthe digital images.

SUMMARY

A machine vision system including a digital camera can be employed toinspect an object in a field of view. One method for inspecting theobject includes capturing, via the digital camera, an original digitalimage including a multiplicity of pixels and associated lightintensities for the field of view including the object. A bitmap imagefile for the original digital image is generated, including themultiplicity of pixels and associated light intensities for the field ofview including the object. A mean value and a standard deviation of thelight intensities of the multiplicity of pixels of the bitmap image filefor the original digital image are dynamically determined using acontroller. New image files are generated, with each new image fileincluding a portion of the multiplicity of pixels having associatedlight intensities within a prescribed range of light intensities definedby the mean value and the standard deviation. Line segments areextracted from each of the new image files, and the extracted linesegments from the new image files are merged and clustered to generateintegral lines based thereon.

The above features and advantages, and other features and advantages, ofthe present teachings are readily apparent from the following detaileddescription of some of the best modes and other embodiments for carryingout the present teachings, as defined in the appended claims, when takenin connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments will now be described, by way of example, withreference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an exemplary vision system including animage detector (camera), a camera controller and an analytic controller,in accordance with the disclosure;

FIG. 2 schematically shows an image feature identification routine foridentifying features of an object from images of a field of view (FOV)captured employing an embodiment of the vision system described withreference to FIG. 1, in accordance with the disclosure;

FIG. 3-1 schematically shows an example of a digital image generatedfrom a bitmap image file for an original digital image that is capturedof an object in a FOV, in accordance with the disclosure;

FIG. 3-2 shows a new digital image that visually displays a first imagefile that is derived from the bitmap image file for the original digitalimage generated for the object shown with reference to FIG. 3-1, inaccordance with the disclosure;

FIG. 3-3 shows another new digital image that visually displays a secondimage file that is derived from the bitmap image file for the originaldigital image generated for the object shown with reference to FIG. 3-1,in accordance with the disclosure;

FIG. 3-4 shows another new digital image that visually displays a thirdimage file that is derived from the bitmap image file for the originaldigital image generated for the object shown with reference to FIG. 3-1,in accordance with the disclosure;

FIG. 3-5 shows another new digital image that visually displays a fourthimage file that is derived from the bitmap image file for the originaldigital image generated for the object shown with reference to FIG. 3-1,in accordance with the disclosure;

FIG. 3-6 shows another new digital image that visually displays a fifthimage file that is derived from the bitmap image file for the originaldigital image generated for the object shown with reference to FIG. 3-1,in accordance with the disclosure;

FIG. 3-7 shows the bitmap image file for the original digital imagegenerated for the object shown with reference to FIG. 3-1 that includesa plurality of merged and clustered line segments extracted from each ofthe images in FIG. 3-2 through 3-6, in accordance with the disclosure;

FIG. 4 graphically shows a first line segment extracted from the imageshown with reference to FIG. 3-2 and a second line segment extractedfrom the image shown with reference to FIG. 3-3 to explain a processstep of merging non-intersecting line segments when the line segmentsare proximal and somewhat parallel, in accordance with the disclosure;and

FIG. 5 a third line segment extracted from the image shown withreference to FIG. 3-2 and a fourth line segment extracted from the imageshown with reference to FIG. 3-3 to explain a process step of clusteringintersecting line segments, in accordance with the disclosure.

DETAILED DESCRIPTION

Referring now to the drawings, wherein the depictions are for thepurpose of illustrating certain exemplary embodiments only and not forthe purpose of limiting the same, FIG. 1 schematically illustrates anexemplary vision system 100 including an image recorder (camera) 10 thatsignally connects to an analytic controller 60.

The camera 10 is preferably a digital image recording device capable ofcapturing a two-dimensional (2D) image 15 of a field of view (FOV) 35.By way of definition, an image is any visually perceptible depictionrepresenting a field of view. An image may encompass all or a portion ofreflected light in the field of view from a visual light spectrum in oneembodiment, including a grayscale reflection, a red-green-blue (RGB)reflection, a black-and-white reflection, or any other suitable ordesirable reflection. Preferably, an image is captured and recorded in anon-transitory storage medium, such as in a non-transitory digital datastorage medium or on photographic film. The camera 10 operates inresponse to a trigger signal, and opens its shutter for a preset shuttertime associated with a preferred exposure time. In one embodiment, thetrigger signal may have a pulsewidth of at least 1 us. The camerashutter speed includes a delay time on the order of less than 10 us. Thepreset shutter time is set for an appropriate exposure time. After thecamera 10 closes the shutter, there may be a delay on the order of 1 msfor data capture, after which the data is transferred to the analyticcontroller 60. The data transfer time to the analytic controller 60 isabout 30 ms, and is a fixed magnitude related to the camera model, whichhas a predetermined image capture and transfer rate (frames per second).Thus, the entire cycle time from start of the trigger to end of datatransfer is less than 40 ms in one embodiment.

The camera 10 can be at any position and orientation relative to the FOV35. In one embodiment, the FOV 35 includes an object 40 oriented on amoveable plane 45 that is at a predetermined distance 22 from the camera10. The object 40 is a structural entity having features including byway of example spatial dimensions, materials and surface finishesindicating reflectivity, among others. In one embodiment, the object 40can be a component or area on a vehicle in an assembly plant.

In one embodiment, the object 40 oriented on the moveable plane 45 ismounted on a first conveying system 42 that conveys the object 40 in alinear path 41 at a known rate of speed, and the camera 10 is mounted ona second conveying system 12 that conveys them in a corresponding linearpath at the known rate of speed for a fixed distance. The linear path 41in which the object 40 and the camera 10 are conveyed includes the FOV35.

In one embodiment, the 2D image 15 is a grayscale image captured by thecamera 10 in a bitmap image file including a multiplicity of pixels,wherein each pixel has an 8-bit value representing a grayscale value.The bitmap image file represents the FOV 35. Other embodiments of the 2Dimage 15 can include a 2D color image represented byHue-Saturation-Intensity (HSI triplets) or Red, Green, and Blue (RGB)primary colors of the FOV 35 or other image representations withoutlimitation. The camera 10 includes an image acquisition sensor thatsignally connects to the camera controller 20 that executes digitalsignal processing (DSP) on the 2D image 15. The image acquisition sensorcaptures a multiplicity of pixels in the FOV 35 at a predeterminedresolution, and the camera controller 20 generates a bitmap image file25 of the FOV 35, e.g., an 8-bit bitmap of the pixels representing theFOV 35 at a predefined resolution, which is communicated to the analyticcontroller 60. The bitmap image file 25 is an encoded datafile stored ina non-transitory digital data storage medium in one embodiment. Thebitmap image file 25 includes a digital representation of the 2D image15 that may include one or a plurality of objects 40 and represents anoriginal image of the FOV 35 captured at the original resolution of thecamera 10. The image acquisition sensor of the camera 10 captures the 2Dimage 15 of the FOV 35 as a multiplicity of pixels at a nominallystandard-definition resolution, e.g., 640×480 pixels. Alternatively, theimage acquisition sensor of the camera 10 may capture the 2D image 15 ata nominally high-definition resolution, e.g., 1440×1024 pixels, or atanother suitable resolution. The image acquisition sensor of the camera10 preferably captures the 2D image 15 in the form of one or a pluralityof still images at the predetermined image capture and transfer rate ofthe camera 10. The 2D image 15 is converted to the bitmap image file 25for storage and analysis in the analytic controller 60.

Controller, control module, module, control, control unit, processor andsimilar terms mean any one or various combinations of one or more ofApplication Specific Integrated Circuit(s) (ASIC), electroniccircuit(s), central processing unit(s) (preferably microprocessor(s))and associated memory and storage (read only, programmable read only,random access, hard drive, etc.) executing one or more software orfirmware programs or routines, combinational logic circuit(s),input/output circuit(s) and devices, appropriate signal conditioning andbuffer circuitry, and other components to provide the describedfunctionality, including data storage and data analysis. Software,firmware, programs, instructions, routines, code, algorithms and similarterms mean any controller-executable instruction sets includingcalibrations and look-up tables.

FIG. 2 schematically shows an image feature identification routine 200for identifying features of an object from images captured employing anembodiment of the vision system 100 described with reference to FIG. 1that includes an image recorder (camera) 10 signally connected to ananalytic controller 60 to capture images of an object 40. Table 1 isprovided as a key wherein the numerically labeled blocks and thecorresponding functions are set forth as follows, corresponding to theimage feature identification routine 200.

TABLE 1 BLOCK BLOCK CONTENTS 210 Capture original digital image of FOVincluding object 212 Calculate average light intensity μ and standarddeviation α for original digital image 214 Generate plurality of newimage files, each new image file associated with a bin, with BIN(k) =μ + k * x * α, for k = −n through n 216 Extract line segments in each ofthe new image files 218 Merge extracted line segments in the new imagefiles to generate integral lines for the original digital image 220Cluster extracted line segments in the new image files to generateintegral lines for the original digital image 222 Export integral lines

The image feature identification routine 200 executes as follows toidentify and digitally extract one or more visibly discernible physicalfeatures from an original digital image that includes an object ofinterest. The image feature identification routine 200 and elementsthereof preferably periodically execute to identify features of anobject from images captured employing an embodiment of the vision system100. In one embodiment, the image feature identification routine 200periodically executes at a rate that is less than 1 second. As usedherein, the terms ‘dynamic’ and ‘dynamically’ describe steps orprocesses that are executed in real-time and are characterized bymonitoring or otherwise determining states of parameters and regularlyor periodically updating the states of the parameters during executionof a routine or between iterations of execution of the routine.

An original digital image of the FOV 35 including the object is capturedat the image capture and transfer rate of the camera 10 (210). FIG. 3-1schematically shows an example of an original digital image 302generated from a bitmap image file that is captured of an object 304 ina FOV 300. The FOV 300 is analogous to the FOV 35 described withreference to FIG. 1. The bitmap image file for the original digitalimage 302 corresponds to the bitmap image file 25 generated withreference to FIG. 1.

Referring again to FIG. 2, the bitmap image file for the originaldigital image 302 of the FOV 300 including the object 304 is in the formof an 8-bit grayscale image at a standard-definition resolution, e.g.,640×480 pixels, of the FOV 300. As such, the bitmap image file includesan 8-bit datapoint for each of the pixels representing light intensityas measured on a 8-bit grayscale, wherein a datapoint having a base tennumerical value of 0 or binary value of 00000000 represents a minimumlight intensity and a datapoint having a base ten numerical value of 255or a binary value of 11111111 represents a maximum light intensity.

The data in the bitmap image file for the original digital image 302 isanalyzed statistically to calculate an average light intensity μ and astandard deviation α of light intensity for the pixels (212).Preferably, this statistical analysis is executed dynamically, e.g., forevery bitmap image file captured by the camera 10. A plurality of newimage files is generated based upon the statistical analysis of thelight intensity data contained in the bitmap image file for the originaldigital image 302 including the average light intensity μ and thestandard deviation α, with each of the new image files including aportion of the data that is separated based upon magnitude of lightintensity (214). This can include generating a quantity of n new imageswith each of the new images associated with pixels in the bitmap imagefile for the original digital image 302 that are within a predefinedrange of light intensity in accordance with the following equation.BIN(k)=μ+k*(x)*α  [1]for k=−n through k=n

wherein k is an integer, and wherein x is a scalar multiplier equal toor less than 1.0, and is calibratable.

This analytical process is employed to generate 2n+1 new digital imagesby separating the light intensity data in the bitmap image file for theoriginal digital image 302 into a plurality of bins BIN(k) using ahistogram process or another suitable data analysis process. As such,the analytical process generates a plurality of one-sided bins. Thus, inan analysis of n=2 for +/−2 standard deviations of light intensity, afirst bin BIN(k=−2) can include that portion of the light intensity datain the bitmap image file that includes all pixels having a lightintensity that is greater than a threshold of μ−2*x*α, a second binBIN(k=−1) can include that portion of the bitmap image file thatincludes all pixels having a light intensity that is greater than athreshold of μ−1*x*α, etc. In each of the new bitmap image files, thepixels in the original digital image 302 that fall outside, i.e., areless than the corresponding threshold, are changed to a value of 0,i.e., blackened. It is appreciated that the scalar multiplier x can beany selectable, calibratable value, and the quantity of new digitalimages that are created and analyzed is based thereon.

FIGS. 3-2 through 3-6 each shows a new digital image derived from thebitmap image file for the original digital image 302 generated for theobject 304 shown with reference to FIG. 3-1, with each of the new imagesassociated with pixels in the bitmap image file for the original digitalimage 302 that are within a predefined range of light intensity asdescribed with reference to Step 114. By way of example, five images areshown for k=−2, k=−1, k=0, k=1 and k=2.

FIG. 3-2 shows a new digital image 322 that visually displays a firstimage file that is derived from the bitmap image file for the originaldigital image 302 generated for the object 304 shown with reference toFIG. 3-1 that includes all pixels having a light intensity that isgreater than μ−2*x*α. Extracted line segments 324 derived from this newdigital image 322 are superimposed thereon.

FIG. 3-3 shows another new digital image 332 that visually displays asecond image file that is derived from the bitmap image file for theoriginal digital image 302 generated for the object 304 shown withreference to FIG. 3-1 that includes all pixels having a light intensitythat is greater than μ−1*x*α. Extracted line segments 334 derived fromthis new digital image 332 are superimposed thereon.

FIG. 3-4 shows another new digital image 342 that visually displays athird image file that is derived from the bitmap image file for theoriginal digital image 302 generated for the object 304 shown withreference to FIG. 3-1 that includes all pixels having a light intensitythat is greater than μ. Extracted line segments 344 derived from thisnew digital image 342 are superimposed thereon.

FIG. 3-5 shows another new digital image 352 that visually displays afourth image file that is derived from the bitmap image file for theoriginal digital image 302 generated for the object 304 shown withreference to FIG. 3-1 that includes all pixels having a light intensitythat is greater than μ+1*x*α. Extracted line segments 354 derived fromthis new digital image 352 are superimposed thereon.

FIG. 3-6 shows another new digital image 362 that visually displays afifth image file that is derived from the bitmap image file for theoriginal digital image 302 generated for the object 304 shown withreference to FIG. 3-1 that includes all pixels having a light intensitythat is greater than μ+2*x*α. Extracted line segments 364 derived fromthis new digital image 362 are superimposed thereon. The scalarmultiplier x associated with a change in light intensity for generatingthe new image files can be user-defined or automatically set to beone-third standard deviation from the average intensity. In oneembodiment, a total of 5 or 7 images are generated with varying lightintensity value, which facilitates extraction of a large group of linesuseable to identify long boundary lines and short strong features of theobject as indicated by the bitmap image file for the original digitalimage 302.

Referring again to FIG. 2, line segments are extracted from each of thenew images (216) using known edge detection techniques. Known edgedetection techniques include a gradient-based method such as a Laplacianoperator, a Canny edge detector, and a Euclidean distance and vectorangle for edge detection in color images. Each line segment is aconnected edge from the edge detection step. The extracted line segmentsrepresent edges of the object in each of the new images. Due to noisyand different light intensity levels, the start and end positions ofeach line segment may be slightly different in each of the images. Thereare many parallel line segments when all images are merged together.Thus, if a line segment is one of the line segments 324 extracted fromthe image 322 in FIG. 3-2, it is likely that an analogous line segmentcan be found in a similar position in the line segments 334, 344 and 354extracted from the images 332, 342 and 352 shown with reference to FIGS.3-3, 3-4 and 3-5, respectively.

Referring again to FIG. 2, extracted ones of the line segments 324, 334,344, 354 and 364 derived from the images 322, 332, 342, 352 and 362shown with reference to FIGS. 3-2 through 3-6 that are overlapping,quasi-parallel, or proximal without intersecting are subjected tomerging (218) by combining the identified line segments to generatehigher pixel density integral line segments associated with the bitmapimage file for the original digital image 302. There may be manyparallel line segments when all the images are merged together. Forexample, if a line segment is identified in image 322, it is likely thatanalogous line segments can be identified in similar positions in images332, 342, 352 and 362.

FIG. 4 graphically shows a first line segment L₁ 410 and a second linesegment L₂ 420 for purposes of explaining merging non-intersecting linesegments when the line segments are proximal and somewhat parallel. Thefirst line segment L₁ 410 is one of the line segments 324 extracted fromthe image 322 shown with reference to FIG. 3-2 and the second linesegment L₂ 420 is one of the line segments 334 extracted from the image332 shown with reference to FIG. 3-3. Line segment L₁ has two end points(P₁ 411, P₂ 412) and line segment L₂ 420 is similarly positioned withtwo end points (P₃ 421 P₄ 422).

Four Euclidean distance values, d₁ 401, d₂ 402, d₃ 403 and d₄ 404 can becalculated between pairs of the two end points (P₁ 411, P₃ 421), (P₁411, P₄ 422), (P₂ 412, P₃ 421), (P₂ 412, P₄ 422), from two line segmentsL₁ 410 and L₂ 420 are calculated. A parallel line distance D_(parallel)can be calculated as followsD _(parallel)=½*(min(d ₁ ,d ₂)+min(d ₃ ,d ₄))   [2]

The parallel line distance D_(parallel) is an average distance of twominimum distances of two of the end points. Additionally a minimum valuemin(d₃, d₄) can be calculated as previous distance measurement ofendpoint P₂ 411 of line L₁ 410. When two lines L₁ 410 and L₂ 420 overlapexactly, the parallel line distance D_(parallel) is zero. When the twolines do not overlap each other, the distance is much larger than theoverlapped case if they have quiet similar length. That characteristicis readily identified when the two lines are parallel. When the parallelline distance D_(parallel) is less than 10% of the length of the smallerof the two line segments, line segment L₁ 410 and line segment L₂ 420are merged to form a single line segment (218). When the parallel linedistance D_(parallel) is less than 50% of the length of the smaller ofthe two line segments, i.e., parallel neighboring line segments, linesegment L₁ 410 is extended and merged with line segment L₂ 420 to form asingle line segment. The parallel line merging is done in a recursivemanner.

Referring again to FIG. 2, extracted ones of the line segments 324, 334,344 and 354 derived from the images 322, 332, 342 and 352 shown withreference to FIGS. 3-2 through 3-5 that intersect or overlap aresubjected to clustering (220) to generate integral lines for the bitmapimage file for the original digital image 302.

FIG. 5 graphically shows a third line segment L₃ 510 and a fourth linesegment L₄ 520 for purposes of explaining clustering of intersectingline segments. The third line segment L₃ 510 is one of the line segments324 extracted from the image 322 shown with reference to FIG. 3-2 andthe fourth line segment L₄ 520 is one of the line segments 334 extractedfrom the image 332 shown with reference to FIG. 3-3. Third line segmentL₃ 510 has two end points (P₁ 511, P₂ 512) and fourth line segment L₄520 is similarly positioned with two end points (P₃ 521 P₄ 522). Anintersecting point P_(c) 525 can be computed for the third line segmentL₃ 510 and fourth line segment L₄ 520, and a new clustered line segment530 consisting of points P₁ 511, P_(c) 525, P₃ 521 can be generated asshown in FIG. 5. The line segment clustering is done in a recursivemanner until all intersected lines are clustered, connected, and groupedtogether. This way, many small line segments that are intersecting willbe combined to be long clustered and connected line segments calledintegral line segments. Integral line segments can be employed inidentifying strong feature(s) on the object.

FIG. 3-7 shows digital image 372 including the original digital image302 generated for the object 304 shown with reference to FIG. 3-1 withthe extracted line segments 324, 334, 344, 354 and 364 derived from theimages shown with reference to FIGS. 3-2 through 3-6 superimposedthereon. Dense line segments emerge from the superimposed extracted linesegments 324, 334, 344, 354 and 364, indicating a boundary for theobject and also indicating strong features. Detecting edges in a seriesof images having varying light intensity levels facilitates edgeextraction. The superimposed extracted line segments 324, 334, 344, 354and 364 have been subjected to merging and clustering.

Referring again to FIG. 2, the integral lines can be exported (222) foruse by other control routines and algorithms, including inspectionroutines. The integral lines may be employed to identify boundaries andstrong features on the object, thus detecting the object by delineatingits shape and quality. Strong features include those features in theimages that are less sensitive to variations in light intensity, i.e.,are viewable under varying light conditions. Thus, strong features arethose features that are consistently present in a plurality of images ofvarying light intensity. Strong features can be employed to identifycomponent boundaries, edges and other elements that can be used tolocate a component in the FOV and evaluate it. By way of example,identified object features can be employed to identify and distinguishan emblem shape and emblem quality. Integral line segment merging andclustering has shown to be able to overcome technical challenges relatedto uncertain feature locations, which may be caused by signally noisyimages. The merging and clustering results can increase the robustnessin the feature recognition without incurring heavy computation time. Inone embodiment, the total computation time for processing three imageswas less than 700 ms. This method will be used in real-time applicationsto identify strong features and outlines for reliable inspectionresults.

The detailed description and the drawings or figures are supportive anddescriptive of the present teachings, but the scope of the presentteachings is defined solely by the claims. While some of the best modesand other embodiments for carrying out the present teachings have beendescribed in detail, various alternative designs and embodiments existfor practicing the present teachings defined in the appended claims.

The invention claimed is:
 1. A method for inspecting an object in afield of view employing a machine vision system including a digitalcamera, comprising: capturing, via the digital camera, an originaldigital image including a multiplicity of pixels and associated lightintensities for the field of view including the object; generating abitmap image file for the original digital image including themultiplicity of pixels and associated light intensities for the field ofview including the object; dynamically determining, using a controller,a mean value and a standard deviation of the light intensities of themultiplicity of pixels of the bitmap image file for the original digitalimage; generating a plurality of new image files, each new image fileincluding a portion of the multiplicity of pixels having associatedlight intensities within a prescribed range of light intensities definedby the mean value and the standard deviation; extracting line segmentsfrom each of the new image files; merging the extracted line segmentsfrom the new image files; clustering the extracted line segments fromthe new image files; and generating integral lines for the object basedupon the merged and clustered extracted line segments from the new imagefiles.
 2. The method of claim 1, wherein generating a plurality of newimage files, each new image file including a portion of the multiplicityof pixels having associated light intensities within a prescribed rangedefined by the mean value and standard deviation comprises generating afirst new image file including a portion of the pixels of the bitmapimage file for the original digital image having light intensities thatare greater than the mean value.
 3. The method of claim 1, whereingenerating a plurality of new image files, each new image file includinga portion of the multiplicity of pixels of the bitmap image file for theoriginal digital image having associated light intensities within aprescribed range defined by the mean value and standard deviationcomprises generating a second new image file including a portion of thepixels of the bitmap image file for the original digital image havinglight intensities that are greater than the mean value plus a firstvalue determined based upon the standard deviation.
 4. The method ofclaim 3, wherein generating a plurality of new image files, each newimage file including a portion of the multiplicity of pixels of thebitmap image file for the original digital image having a lightintensity that is within a prescribed range defined by the mean valueand standard deviation comprises generating a third new image fileincluding a portion of the pixels of the bitmap image file for theoriginal digital image having light intensities that are greater thanthe mean value plus a second value determined based upon the standarddeviation.
 5. The method of claim 1, wherein extracting line segmentsfrom each of the new image files comprises employing edge detection toextract the line segments from each of the new image files.
 6. Themethod of claim 1, wherein merging the extracted line segments from thenew image files comprises combining proximal, non-intersecting ones ofthe extracted line segments from the new image files.
 7. The method ofclaim 1, wherein clustering the extracted line segments from the newimage files comprises combining intersecting ones of the extracted linesegments from the new image files.
 8. The method of claim 1, furthercomprising delineating a shape of the object in the field of view byidentifying boundaries and strong features on the object.
 9. The methodof claim 8, wherein identifying boundaries and strong features on theobject comprises identifying those features that are present in all ofthe new image files associated with varying light intensities.
 10. Amethod for detecting an object in a field of view employing a digitalcamera, comprising: capturing, via the digital camera, a grayscaledigital image including a multiplicity of pixels and associatedgrayscale light intensities for the field of view including the object;generating a bitmap image file for the original digital image includingthe multiplicity of pixels and associated grayscale light intensitiesfor the field of view; dynamically determining, using a controller, amean value and a standard deviation of the grayscale light intensitiesof the multiplicity of pixels of the bitmap image file for the originaldigital image; generating a plurality of new images, each new imageincluding a portion of the multiplicity of pixels having associatedgrayscale light intensities within a prescribed range of grayscale lightintensities defined by the mean value and the standard deviation;extracting line segments from each of the new images; merging theextracted line segments from the new images; clustering the extractedline segments from the new images; generating integral lines based uponthe merged and clustered extracted line segments from the new images;and identifying boundaries of the object based upon the generatedintegral lines.
 11. The method of claim 10, wherein generating aplurality of new image files, each new image file including a portion ofthe multiplicity of pixels having associated grayscale light intensitieswithin a prescribed range defined by the mean value and standarddeviation comprises generating a first new image file including aportion of the pixels of the bitmap image file for the original digitalimage having grayscale light intensities that are greater than the meanvalue.
 12. The method of claim 10, wherein generating a plurality of newimage files, each new image file including a portion of the multiplicityof pixels of the bitmap image file for the original digital image havingassociated grayscale light intensities within a prescribed range definedby the mean value and standard deviation comprises generating a secondnew image file including a portion of the pixels of the bitmap imagefile for the original digital image having grayscale light intensitiesthat are greater than the mean value plus a first value determined basedupon the standard deviation.
 13. The method of claim 12, whereingenerating a plurality of new image files, each new image file includinga portion of the multiplicity of pixels of the bitmap image file for theoriginal digital image having a grayscale light intensity that is withina prescribed range defined by the mean value and standard deviationcomprises generating a third new image file including a portion of thepixels of the bitmap image file for the original digital image havinggrayscale light intensities that are greater than the mean value plus asecond value determined based upon the standard deviation.
 14. Themethod of claim 10, wherein extracting line segments from each of thenew image files comprises employing edge detection to extract the linesegments from each of the new image files.
 15. The method of claim 10,wherein merging the extracted line segments from the new image filescomprises combining proximal, non-intersecting ones of the extractedline segments from the new image files.
 16. The method of claim 10,wherein clustering the extracted line segments from the new image filescomprises combining intersecting ones of the extracted line segmentsfrom the new image files.
 17. The method of claim 10, whereinidentifying boundaries of the object based upon the generated integrallines comprises delineating a shape of the object in the field of viewbased upon the identified boundaries.
 18. The method of claim 17,further comprising identifying strong features on the object comprisingidentifying those features that are present in all of the new imagefiles associated with varying grayscale light intensities.
 19. A machinevision system for inspecting an object in a field of view, comprising: adigital camera; and an analytic controller, wherein the analyticalcontroller executes an image feature identification routine including:capturing, via the digital camera, an original digital image including amultiplicity of pixels and associated light intensities for the field ofview including the object, generating a bitmap image file for theoriginal digital image including the multiplicity of pixels andassociated light intensities for the field of view including the object,dynamically determining, using a controller, a mean value and a standarddeviation of the light intensities of the multiplicity of pixels of thebitmap image file for the original digital image, generating a pluralityof new image files, each new image file including a portion of themultiplicity of pixels having associated light intensities within aprescribed range of light intensities defined by the mean value and thestandard deviation, extracting line segments from each of the new imagefiles, merging the extracted line segments from the new image files,clustering the extracted line segments from the new image files, andgenerating integral lines for the object based upon the merged andclustered extracted line segments from the new image files.